Q21UB Wiki
='Welcome to the Special Topics in Communications: Networks Q21UB Wiki'= Instructions The final project (required only from the graduate students) is a student-selected research project that applies networking principles in our everyday applications by looking at how our world is connected socially, strategically and technologically and why it matters. In other words you need to propose a Q21 similar to the 20Q to be covered in this course that utilizes networking concepts to address the proposed question. The proposal should be 1-2 pages and should provide a detailed description of the question you are trying to address. You should convince the reader that the problem addressed plays a significant role in our networked life. You should also state what you plan to deliver at the end of the project. You should include a short list of references that are related to your project. If it is a joint project, you should state the planned division of work. I will give you feedback on your proposal. Be careful not to propose an overly ambitious project since everything must be done within the semester. The final project could be a collaborative project with at most two students. If a collaborative project is pursued, the scope of the project is expected to be about twice that of a one-student project, and the works of each student must be clearly stated so that I can grade them separately. Start immediately with your project! Think about what you want to do. You are encouraged to discuss with the instructor about your plans.. Wiki page You need to create a wiki page for your proposed Q21. It should be similar format to the course book chapters but not included the advanced materials. In your wiki page you need to create 2 examples with solutions similar to the ones in the 20Q textbook. ='Question 21'= How can I trust a digital transaction made using Bitcoins? by Enrico Santagati Bitcoin (BTC), is a decentralized digital currency based on an open-source, peer-to-peer internet protocol and it was introduced in 2009 by a pseudonymous developer named Satoshi Nakamoto. Today, Bitcoin is the most widely used alternative currency and already a few hundreds of companies and individuals are accepting Bitcoin as a form of payment. Nevertheless, Is Bitcoin robust and efficient enough to be used as an every-day currency? Read the Full Article How does my GPS determine my route? by Colleen Bailey We often take for granted how easy it is to navigate today. Many people have handheld GPS devices and even more have a smart phone with GPS capability. All you need is a destination point and you receive step by step guidance from your current location. Read the Full Article How do matching sites do the matching? by Xiangyu Chen § A short answer A matching problem can be formulated by the Stable Marriage problem.(One of the classic matching problem), and it can be formulated by the stable fixture problem. The stable fixtures (SF) problem is a generalisation of the stable roommates (SR) problem (a detailed treatment of which can be found in which each participant has a fixed capacity, and is to be assigned a number of matches less than or equal to that capacity subject to the normal stability criterion. Read more Category:Browse Category:2.21 What is the technology behind Google and why people choose Google instead of Bing or other search engines? Category:Browse Category:2.21 What is the technology behind Google and why people choose Google instead of Bing or other search engines? How to Capture the Zeitgeist via Eigen-tweets? by Emrecan Demirors, Panos Markopoulos How can GPS devices be tracked from anywhere in the world? by Adam Gannon The Automatic Packet Reporting System (APRS) is a digital communication system for real-time information exchange between amateur radio users in a local area. Now over 30 years old the network has grown to accomidate 40,000 users. A complex web of digital repeaters and internet connected relay stations allows users to transmit data packets, the most common type being GPS position, well beyond the range of a conventional radio. In a network such as this, how do we manage congestion while giving each user the chance to reliably transmit as much data as they need to? What are the challenges encountered and solutions presented with maintaining this unique network? Read the full article How does Pandora differentiate my musical taste? by Luigi Di Tacchio, Zahed Hossain Why the current 4G is much faster than the previous generations? by Zimu Guo Today, everyone need a connection to the internet. People both need to learn news from the internet and to watch video for entertainment. Considering none wants to wait for minutes to read one slide of news and for hours to buffer a movie. A technology which can provide a high transmission rate is needed on edge. 4G connection is such a technology which can provide a downlink speed up to 1 Gbits/s. Such a high rate can be achieved by implementing MIMO technology. MIMO system is the used widely today to provide a high speed data transmission. The use of multiple transmitting and receiving antennas can provide high spectral efficiency and link reliability for point-to-point communication in fading environments. The difference between SISO and MIMO system compared here is the BER (bit error rate). The lower BER, the better transmission rate. With the same energy constrain, the system with lower BER can achieve a higher transmission rate which we can say the data transmission is faster. Read More What’s the deal with Internet rush hour? by Robert Ward, John Inzina Is Cloud Computing Green? by Nan Cen, Peiran Song ---- Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Green cloud computing, refers to environmentally sustainable computing. Murugesan defines the field of green computing as 'the study and practice of designing, manufacturing, using and disposing of computers, servers and associated subsystems - such as monitors, printers, storage devices and networking and communication systems - efficiently and effectively with minimal or no impact on the environment'. Modern IT systems are complicated because all of them rely on so many factors such as application or software, people, networks and hardware. A solution may also need to address end-user satisfaction, management restructuring, regulatory compliance and return on investment (RIO). To reduce the use of hazardous materials, maximize energy efficiency during the product's lifetime, and promote the recyclability or biodegradability of defunct products and factory waste are the main goals of green computing, which can be attained by making use of computers as energy-efficient as possible and designing algorithms and systems for efficiency-related computer technologies. Read the Full Article Why do I pay 7.99$ monthly for Netflix? by Sunit Kapuria, Cédric Lolliot How Does Facebook News Feed Work? by Khoi Nguyen, Viral Patel Facebook was created originally in Harvard by Mark Zuckerberg with its sole purpose to share pictures and class notes within Harvard students. However, it quickly expanded and became one of the most social visit sites today. Almost everyone, especially students from middle school to college to working business people have at least one Facebook account. With its expansion, each Facebook user eventually has added from hundreds to thousands of friends. Read More Did Facebook Wipe Out Orkut or was it Google Itself? by Rachana Nikum, Pragya Tiwari 'How can Social Media like Twitter be used to predict future events like elections?' Alesha Patel, Meghna Vaidya 1.1 Introduction :: We start by looking at the existing methodologies for measuring the beliefs and intentions of individuals, for example market surveys and polls. These polls have a number of disadvantages related to them in terms of cost, time and human effort involved. But thanks to social media sites like Twitter, Facebook etc.; we have a large amount of information available on the internet about every topic, or major events or happenings in different parts of the world- in the form of people’s sentiments on the social media sites. The intuitive idea is to utilize this user generated information to produce the results which are traditionally published using surveys and polls.This is a challenging task. We need to ensure that our sample has representative distribution. We have to take into account the sentiments in the user-generated content. Amongst all these factors, we have one feature in our favor – Wisdom of Crowds. '''The concept of wisdom of crowds tells us that the error reduces by a factor as large as the crowd when we average the estimates first. But we have to take into account a lot of other factors, along with Wisdom of Crowds. :: We have to take into account the following factors: *Appropriate '''Sampling Approaches (to make sure the sample population is representative) * Methods of Modeling Political Sentiment *''' 'Incorporating the notion of Political Sentiment''' in our Prediction Model Once we have developed the model, we compare its prediction outcomes with the traditional polls and the actual election outcomes. In scenarios like result forecasting, the final result is used to judge the accuracy of the underlying prediction model; rather than the continuous time data. But, there are concerns about using Twitter as a reliable source of data; with two major ones being inability to determine the representative sample accurately and potential for deliberately influencing the results using spamming etc. Hence, these predictive schemes seem to be both promising and challenging. 1.2 Short Answer In order to further our understanding of the method of election predictions using Twitter, we focus on the 2011 Irish General Election to model political sentiment through the mining of social media. These elections were held on February 25th, 2011. There were five major political parties: Fianna Fail(FF), The Green Party, Labour, Fina Gael(FG) and Sinn Fein(SF). 32,578 tweets relevant to these five parties were collected between February 8th and February 25th. The relevant tweets were identified using the parties names and abbreviations, and the election hashtag #ge11. The prediction error is measured in terms of Mean Average Error (MAE) , i.e. the average of the errors in each forecast. MAE measures the deviation between the predicted values from the actual values. It is used to measure the error between Twitter predictions and actual results; as well as Twitter predictions and polls. In order to predict the election results, we would like to base our assumptions on two factors: '''V'olume and Sentiment'. It is only logical to assume that larger the volume of related content (number of tweets) for a given political party; more will be the number of votes that party receives. This can be justified as follows- large volume of content would mean the party would attract more attention and will have more number of candidates; and hence, more number of votes. This leads to a conclusion that larger parties will have a larger presence and a large vote bank as compared to smaller parties. But is this the correct measure of popularity? Volume could be easily affected by a few very prominent stories or deliberate spamming. Thus, the volume based measure of popularity has to be defined carefully. Our volume-based measure is the ratio of number of tweets relevant for a given party and sum of tweets relevant for all the parties; it can be written as follows: : '''Where ''SoV(x) represents the Share of Volume for a given party x.' : 'N'' is the total number of parties (in our case, this number is 5) ' : Rel(x) is the number of tweets relevant to party x. ''' :: This formula has an advantage that the Share of Volume for all the parties sum upto one; hence leading to easier analysis and comparisons. : We use different sample sizes (to make sure that our sample is representative and to find the best sample out of all). These are as follows '''Time- Based: Most recent ones- ranging from 24 hours, 3 days and 7 days. Sample-Size Based: Most recent 1000, 2000, 5000 or 10000 tweets. Cumulative: All tweets from February 8th to relevant time. Manual: Manually labeled tweets from February 8th. The second aspect of prediction is Sentiment Analysis. Previous research has shown that supervised learning provides more accurate sentiment analysis as compared to unsupervised methods. So its better to use trained classifiers for the elections. Also, different trained annotators should be used; and the data should be collected over varied time (to make sure the sample is diverse enough). In our example, the annotation categories are as follows: **Three''' Sentiment Classes''' (Positive, Negative, Mixed). **One Non-Sentiment Class (Neutral). **Three other Classes (Unannotatable, Non-relevant, Unclear ). These three classes, Unannotatable, Non-relevant and Unclear respectively, are disregarded. The mixed annotations are also disregarded because they are few in number and unambiguous. Various Socio-linguistic features like emoticons and unconventional punctuations are also taken into account, because these features add tone to the text and are likely to add the value to the sentiment. All the topic terms, usernames and url’s are removed to make the classification as unbiased as possible. After deciding how to classify the tweets according to different sentiments, next step is to decide how to incorporate this sentiment into the prediction model. Sentiment Distribution in the tweets for a given party indicates the disposition of people towards that particular political party. If majority of the tweets have a negative sentiment, it is likely that people have a negative inclination towards that party. But, this is true for a party in isolation. But we are considered all the political parties involved in the election at the same time. In a closed system like election, relative sentiment between different parties becomes much more important. *: To address the Relative Sentiment issue, we modify the SoV (Share of Volume) Parameter, to represent the share of positive and negative volume as follows: where SoVP is the share of positive volume; SoVn is the share of negative volume; Pos (x) is the number of tweets with a positive sentiment for a given party x; Neg(x) is the number of tweets with a negative sentiment for a given party x; n'' is the total number of parties. ' This is the Inter Party Sentiment'. For' Intra Party Sentiment', we use a' log-ratio sentiment''' as follows: This tells us how positive or negative the tweets are for a given topic. Its value is positive when the number of positive tweets is more than the number of negative tweets; and is negative when the number of negative tweets is greater than the number if positive tweets. How does Google Maps make our life easy? by Rachita Patrikar, Sivaram Rajendhran How VRCodes reboot smartcodes? by Georgios Sklivanitis How do travel sites recommend hotels?: Multi-dimensional Recommender Systems by Marcia Torrico 'The need for Multi-dimensional Recommender Systems' Recommendation System Based on Social Relations by Yaswanth Varadappavari Does it matter how far you are in a virtual world network? by Krutish Venkataraman What makes LinkedIn so popular? by Mahmuda Zafrin Why do Internet Exchange Points Play a Fundamental Role in the Internet Ecosystem? by Mirko Gradillo Category:Browse Category:2.21 What is the technology behind Google and why people choose Google instead of Bing or other search engines?